Generalized Linear Models

By S. Heritier, Eva Cantoni, S. Copt, Maria-Pia Victoria-Feser. 2010

1,394 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ee005c9008ff1ab0264531aaa6001259dc3b5b69

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Citations in the corpus

Bayesian Poisson regression for crowd counting (2009, background): “0) [8] 15 (±4.”
Fast Poisson estimation with high-dimensional fixed effects (2019, methodology): “GLMs are a class of regression models based on the exponential family of distributions that were introduced by Nelder and Wedderburn (1972).”
Optimal errors and phase transitions in high-dimensional generalized linear models (2017, background): “is discrete and corresponds to the labels that classify the data-points Φμ [1, 2, 7].”
Extending Extended Logistic Regression: Extended versus Separate versus Ordered versus Censored (2014, methodology): “Originally it is a regression model from the generalized linear model framework (Nelder and Wedderburn 1972) to model the probability of binary responses: P(y# qj j x)5 exp(xTb) 11 exp(xTb) 5L(xTb) , (1) where b 5 (b0, b1, b2, . . .) T is a coefficient vector and L( ) 5 exp( )/[1 1 exp( )] is…”
Bayesian information criteria and smoothing parameter selection in radial basis function networks (2004, methodology): “Preamble Section 3·2 presents a radial basis function network regression model and derives a Bayesian information criterion in the context of generalised linear models (Nelder & Wedderburn, 1972; McCullagh & Nelder, 1989, Ch. 2).”
Models for Overdispersed Data in Entomology (2014, methodology): “Model comparison and inference for generalized linear models is based on the Analysis of deviance, generalizing ideas from ANOVA and first introduced by Nelder and Wedderburn (1972).”
A Family of Generalized Linear Models for Repeated Measures With Normal and Conjugate Random Effects (pp.188) (2010, methodology): “Schall (1991) proposed an efficient and general estimation algorithm, based on Harville’s (1974) modification of Henderson’s (1984) mixed-model equations.”
Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “Early methods, such as collaborative filtering (CF) [146], logistic regression (LR) [124], factorization machine (FM) [141], and gradient boosting decision tree (GBDT) [78], have been intensively used in practical applications due to the efficiency and interpretability.”
Two‐component mixtures of normal, gamma, and Gumbel distributions for hydrological applications (2011, unknown): “…that is ytj ; ; d ¼ 2 Ga? ð ; Þ if m ¼ 1, with a probability density function defined by f ðytj ; ; 2Þ ¼ ð Þ y 1 t expð yt= Þ; 0 yt <1: This distribution Ga?ð ; Þ [see Nelder and Wedderburn, 1972; Morris, 1982] must be distinguished from the distribution denoted by Gað ; Þ (with the mean given…”
Count Time Series: A Methodological Review (2021, background): “The unification of discrete and continuous paradigms began in Nelder and Wedderburn (1972) and was further developed in McCullagh and Nelder (1989) for independent series.”
Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma (2015, background): “Celebrated examples include linear, logistic, multinomial regressions and applications to graphical models (Nelder and Baker, 1972; McCullagh and Nelder, 1989; Koller and Friedman, 2009).”
The influence of below-ground herbivory and defoliation of a legume on nitrogen transfer to neighbouring plants. (2007, methodology): “Data were analysed using generalized linear models (Nelder & Wedderburn 1972) with the GENMOD procedure in sas ver.”
Synoptic and meteorological drivers of extreme ozone concentrations over Europe (2016, methodology): “Logistic regression is a special case of generalized linear models (Nelder and Wedderburn 1972, McCullagh and Nelder 1989), which is a generalization of classical linear regression.”